Evaluation and update of the expert consensus guidelines for the assessment of the cortisol awakening response (CAR)
Bibliographic record
Abstract
The cortisol awakening response (CAR) is frequently assessed in psychobiological (stress) research. Obtaining reliable CAR data, however, requires careful attention to methodological detail. To promote best practice, expert consensus guidelines on the assessment of the CAR were published (Stalder et al., 2016, PNEC). However, it is unclear whether these highly cited guidelines have resulted in actual methodological improvements. To explore this, the PNEC editorial board invited the present authors to conduct a critical evaluation and update of current CAR methodology, which is reported here. (i) A quantitative evaluation of methodological quality of CAR research published in PNEC before and after the guidelines (2013-2015 vs. 2018-2020) was conducted. Disappointingly, results reveal little improvement in the implementation of central recommendations (especially objective time verification) in recent research. (ii) To enable an update of guidelines, evidence on recent developments in CAR assessment is reviewed, which mostly confirms the accuracy of the majority of the original guidelines. Moreover, recent technological advances, particularly regarding methods for the verification of awakening and sampling times, have emerged and may help to reduce costs in future research. (iii) To aid researchers and increase accessibility, an updated and streamlined version of the CAR consensus guidelines is presented. (iv) Finally, the response of the PNEC editorial board to the present results is described: potential authors of future CAR research to be published in PNEC will be required to submit a methodological checklist (based on the current guidelines) alongside their article. This will increase transparency and enable reviewers to readily assess the quality of the respective CAR data. Combined, it is hoped that these steps will assist researchers and reviewers in assuring higher quality CAR assessments in future research, thus yielding more reliable and reproducible results and helping to further advance this field of study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".